Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …

Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

Learning partial differential equations'(PDEs) solution operators is an essential problem in
machine learning. However, there are several challenges for learning operators in practical …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Vision transformers have delivered tremendous success in representation learning. This is
primarily due to effective token mixing through self attention. However, this scales …

Recently, large-scale pre-training methods like CLIP have made great progress in multi-
modal research such as text-video retrieval. In CLIP, transformers are vital for modeling …

Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …

Partial differential equations (PDEs) see widespread use in sciences and engineering to
describe simulation of physical processes as scalar and vector fields interacting and …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …